We introduce a new methodology for the hierarchical modeling of the behavior-with-time of players operating and interacting within a certain application domain. Behavior modelling and characterization are performed online, given that a number of observations are made or sensed at regular time intervals with respect to each player. A key element of this hierarchical behavior modeling system architecture is a new formulation of multiple hidden Markov models (HMM) with discrete densities operating in parallel, with each HMM accepting a single feature-related observation sequence. However the proposed classification approach recognizes the existence of possible dependencies between the observation sequences of the features obtained for a given player. This property is effectively exploited in a new dependent-multiHMM with discrete densities (DM-HMM-D) classification approach. The proposed methodology is applied in modeling the behavior of aircrafts operating in relatively simple 3D "air-patrol" situations. Computer simulation results demonstrate the significant gains that can be obtained in system classification and modeling performance when compared to those obtained while using conventional independent-multidiscrete hidden Markov model (IM-HMM-D) schemes.